SensoMineR (version 1.27)

ardi: Automatic Research of DIvergences between scores

Description

Spot the most singular or particular data with respect to all descriptors and to two qualitative variables and all their possible categories combinations.
Computes the highest differences between all the categories of the variables product, panelist and all their possible combinations, with respect to a set of quantitative variables (the sensory descriptors).

Usage

ardi(donnee, col.p, col.j, firstvar, lastvar = ncol(donnee), 
      nbval = 10, center = TRUE, scale = FALSE)

Value

A list containing the following elements:

tab

a data frame (descriptors are mean centered per panelist and scaled to unit variance)

panelist

a data frame, by default the 10 highest divergences between panelists according to the sensory descriptors

product

a data frame, by default the 10 highest divergences between products according to the sensory descriptors

combination

a data frame, by default the 10 highest divergences between panelists and products according to the sensory descriptors

Arguments

donnee

a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors)

col.p

the position of the product variable

col.j

the position of the panelist variable

firstvar

the position of the first sensory descriptor

lastvar

the position of the last sensory descriptor (by default the last column of donnee)

nbval

the number of highest divergences to be displayed

center

by default, data are mean centered by panelist

scale

by default, data are not scaled by panelist

Author

F Husson, S Le

Details

Step 1 For each quantitative variable, means by all the possible combinations (panelist,product) are computed.
Step 2 Then, data are mean centered and scaled to unit variance by descriptor and the divergence corresponds to the absolute value of the entries.
Step 3 Means on divergences are computed by products or by panelists and then sorted.

See Also

decat

Examples

if (FALSE) {
data(chocolates)
ardi(sensochoc, col.p = 4, col.j = 1, firstvar = 5)
}